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Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants

Author

Listed:
  • Belqasem Aljafari

    (Department of Electrical Engineering, College of Engineering, Najran University, Najran 11001, Saudi Arabia)

  • Siva Rama Krishna Madeti

    (Department of Electrical and Electronics Engineering, SRKR Engineering College, Bhimavaram 534204, India)

  • Priya Ranjan Satpathy

    (Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India)

  • Sudhakar Babu Thanikanti

    (Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India)

  • Bamidele Victor Ayodele

    (Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia)

Abstract

In this paper, a novel fault detection and diagnosis technique for a grid-tied photovoltaic (GTPV) with the ability of module-level fault location and differentiation is proposed. The proposed system measures the voltage, current, and temperature of the PV modules using low-cost sensors and critically compares them with the mathematical evaluated data to locate the type and location of the fault in the system. Additionally, a power line communication (PLC)-based low-cost PV monitoring system for tracking the operation of individual modules along with a fault detection algorithm is proposed to detect and locate the fault in the system. An intuitive online web application is also created to make it simple for users to view monitored data online. The suggested method is shown to have reduced computing needs; thus, the transmission of data and fault diagnosis is performed using the same microcontroller without the need for extra hardware or simulation software. The usefulness of the proposed method in identifying different fault occurrences in GTPV systems has been shown via experimental findings.

Suggested Citation

  • Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7789-:d:948939
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    References listed on IDEAS

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    1. Dusonchet, Luigi & Telaretti, Enrico, 2010. "Economic analysis of different supporting policies for the production of electrical energy by solar photovoltaics in eastern European Union countries," Energy Policy, Elsevier, vol. 38(8), pages 4011-4020, August.
    2. Aljafari, Belqasem & Satpathy, Priya Ranjan & Thanikanti, Sudhakar Babu, 2022. "Partial shading mitigation in PV arrays through dragonfly algorithm based dynamic reconfiguration," Energy, Elsevier, vol. 257(C).
    3. Dhimish, Mahmoud & Holmes, Violeta & Dales, Mark, 2017. "Parallel fault detection algorithm for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 113(C), pages 94-111.
    4. Verma, Deepak & Nema, Savita & Shandilya, A.M. & Dash, Soubhagya K., 2016. "Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1018-1034.
    5. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Monitoring system for photovoltaic plants: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1180-1207.
    6. Dusonchet, Luigi & Telaretti, Enrico, 2010. "Economic analysis of different supporting policies for the production of electrical energy by solar photovoltaics in western European Union countries," Energy Policy, Elsevier, vol. 38(7), pages 3297-3308, July.
    7. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Online fault detection and the economic analysis of grid-connected photovoltaic systems," Energy, Elsevier, vol. 134(C), pages 121-135.
    8. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    9. Mani, Monto & Pillai, Rohit, 2010. "Impact of dust on solar photovoltaic (PV) performance: Research status, challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3124-3131, December.
    10. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    11. Siddiqui, Rahnuma & Kumar, Rajesh & Jha, Gopal Kumar & Gowri, Ganesh & Morampudi, Manoj & Rajput, Pragati & Lata, Sneh & Agariya, Swati & Dubey, Bharat & Nanda, Gayatri & Raghava, Sykam Sahan, 2016. "Comparison of different technologies for solar PV (Photovoltaic) outdoor performance using indoor accelerated aging tests for long term reliability," Energy, Elsevier, vol. 107(C), pages 550-561.
    12. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
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